


"""simple docstring"""


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__lowerCamelCase   :List[Any]						     =		logging.get_logger(__name__)

__lowerCamelCase   :Tuple						     =		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase   :Dict						     =		{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__lowerCamelCase   :List[Any]						     =		{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__lowerCamelCase   :List[str]						     =		{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase   :str						     =		{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__lowerCamelCase   :Any						     =		{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__lowerCamelCase   :List[str]						     =		{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__lowerCamelCase   :Tuple						     =		{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCamelCase   :str						     =		{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCamelCase   :Tuple						     =		{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}


class       A__						(    lowercase__):


     """simple docstring"""
     snake_case__						:       Dict				    =VOCAB_FILES_NAMES
     snake_case__						:       Optional[int]				    =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     snake_case__						:       Optional[int]				    =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__						:       int				    =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     snake_case__						:       int				    =DPRContextEncoderTokenizer


class       A__						(    lowercase__):


     """simple docstring"""
     snake_case__						:       Union[str, Any]				    =VOCAB_FILES_NAMES
     snake_case__						:       Optional[Any]				    =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     snake_case__						:       Union[str, Any]				    =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__						:       Any				    =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     snake_case__						:       Tuple				    =DPRQuestionEncoderTokenizer


__lowerCamelCase   :Tuple						     =		collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__lowerCamelCase   :Optional[Any]						     =		collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__lowerCamelCase   :Union[str, Any]						     =		r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '


@add_start_docstrings(lowercase__)
class       A__						:


     """simple docstring"""

     def __call__(						self:    List[Any]  ,		__a:    List[str]  ,		__a:    Optional[str] = None  ,		__a:    Optional[str] = None  ,		__a:    Union[bool, str] = False  ,		__a:    Union[bool, str] = False  ,		__a:    Optional[int] = None  ,		__a:    Optional[Union[str, TensorType]] = None  ,		__a:    Optional[bool] = None  ,		**__a:    int  ,		)->     BatchEncoding:


           if titles is None and texts is None:
                 return super().__call__(
                     SCREAMING_SNAKE_CASE_  ,		padding=SCREAMING_SNAKE_CASE_  ,		truncation=SCREAMING_SNAKE_CASE_  ,		max_length=SCREAMING_SNAKE_CASE_  ,		return_tensors=SCREAMING_SNAKE_CASE_  ,		return_attention_mask=SCREAMING_SNAKE_CASE_  ,		**SCREAMING_SNAKE_CASE_  ,		)
           elif titles is None or texts is None:
                 lowerCamelCase       :       str   =						titles if texts is None else texts
                 return super().__call__(
                     SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_  ,		padding=SCREAMING_SNAKE_CASE_  ,		truncation=SCREAMING_SNAKE_CASE_  ,		max_length=SCREAMING_SNAKE_CASE_  ,		return_tensors=SCREAMING_SNAKE_CASE_  ,		return_attention_mask=SCREAMING_SNAKE_CASE_  ,		**SCREAMING_SNAKE_CASE_  ,		)
           lowerCamelCase       :       Tuple   =						titles if not isinstance(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_						) else [titles]
           lowerCamelCase       :       Union[str, Any]   =						texts if not isinstance(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_						) else [texts]
           lowerCamelCase       :       Union[str, Any]   =						len(SCREAMING_SNAKE_CASE_						)
           lowerCamelCase       :       Any   =						questions if not isinstance(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_						) else [questions] * n_passages
           assert len(SCREAMING_SNAKE_CASE_						) == len(
               SCREAMING_SNAKE_CASE_						), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_						)} titles and {len(SCREAMING_SNAKE_CASE_						)} texts.'
           lowerCamelCase       :       Optional[int]   =						super().__call__(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_  ,		padding=SCREAMING_SNAKE_CASE_  ,		truncation=SCREAMING_SNAKE_CASE_						)["""input_ids"""]
           lowerCamelCase       :       List[Any]   =						super().__call__(SCREAMING_SNAKE_CASE_  ,		add_special_tokens=SCREAMING_SNAKE_CASE_  ,		padding=SCREAMING_SNAKE_CASE_  ,		truncation=SCREAMING_SNAKE_CASE_						)["""input_ids"""]
           lowerCamelCase       :       Union[str, Any]   =						{
               """input_ids""": [
                   (encoded_question_and_title + encoded_text)[:max_length]
                   if max_length is not None and truncation
                   else encoded_question_and_title + encoded_text
                   for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_  ,		SCREAMING_SNAKE_CASE_						)
               ]
           }
           if return_attention_mask is not False:
                 lowerCamelCase       :       int   =						[]
                 for input_ids in encoded_inputs["input_ids"]:
                       attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
                 lowerCamelCase       :       Optional[Any]   =						attention_mask
           return self.pad(SCREAMING_SNAKE_CASE_  ,		padding=SCREAMING_SNAKE_CASE_  ,		max_length=SCREAMING_SNAKE_CASE_  ,		return_tensors=SCREAMING_SNAKE_CASE_						)

     def 		a__    (						self:    str  ,		__a:    BatchEncoding  ,		__a:    DPRReaderOutput  ,		__a:    int = 16  ,		__a:    int = 64  ,		__a:    int = 4  ,		)->     List[DPRSpanPrediction]:
           lowerCamelCase       :       Optional[int]   =						reader_input["""input_ids"""]
           lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       List[Any]   =						reader_output[:3]
           lowerCamelCase       :       str   =						len(SCREAMING_SNAKE_CASE_						)
           lowerCamelCase       :       Optional[Any]   =						sorted(range(SCREAMING_SNAKE_CASE_						)  ,		reverse=SCREAMING_SNAKE_CASE_  ,		key=relevance_logits.__getitem__						)
           lowerCamelCase       :       str   =						[]
           for doc_id in sorted_docs:
                 lowerCamelCase       :       Optional[int]   =						list(input_ids[doc_id]						)
                 # assuming question & title information is at the beginning of the sequence
                 lowerCamelCase       :       Optional[Any]   =						sequence_ids.index(self.sep_token_id  ,		2						) + 1  # second sep id
                 if sequence_ids[-1] == self.pad_token_id:
                       lowerCamelCase       :       str   =						sequence_ids.index(self.pad_token_id						)
                 else:
                       lowerCamelCase       :       List[Any]   =						len(SCREAMING_SNAKE_CASE_						)

                 lowerCamelCase       :       Union[str, Any]   =						self._get_best_spans(
                     start_logits=start_logits[doc_id][passage_offset:sequence_len]  ,		end_logits=end_logits[doc_id][passage_offset:sequence_len]  ,		max_answer_length=SCREAMING_SNAKE_CASE_  ,		top_spans=SCREAMING_SNAKE_CASE_  ,		)
                 for start_index, end_index in best_spans:
                       start_index += passage_offset
                       end_index += passage_offset
                       nbest_spans_predictions.append(
                           DPRSpanPrediction(
                               span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]  ,		relevance_score=relevance_logits[doc_id]  ,		doc_id=SCREAMING_SNAKE_CASE_  ,		start_index=SCREAMING_SNAKE_CASE_  ,		end_index=SCREAMING_SNAKE_CASE_  ,		text=self.decode(sequence_ids[start_index : end_index + 1]						)  ,		)						)
                 if len(SCREAMING_SNAKE_CASE_						) >= num_spans:
                       break
           return nbest_spans_predictions[:num_spans]







     def 		a__    (						self:    List[str]  ,		__a:    List[int]  ,		__a:    List[int]  ,		__a:    int  ,		__a:    int  ,		)->     List[DPRSpanPrediction]:
           lowerCamelCase       :       Optional[Any]   =						[]
           for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_						):
                 for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
                       scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
           lowerCamelCase       :       Tuple   =						sorted(SCREAMING_SNAKE_CASE_  ,		key=lambda __a						: x[1]  ,		reverse=SCREAMING_SNAKE_CASE_						)
           lowerCamelCase       :       List[Any]   =						[]
           for (start_index, end_index), score in scores:
                 assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
                 lowerCamelCase       :       Tuple   =						end_index - start_index + 1
                 assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
                 if any(
                     start_index <= prev_start_index <= prev_end_index <= end_index
                     or prev_start_index <= start_index <= end_index <= prev_end_index
                     for (prev_start_index, prev_end_index) in chosen_span_intervals						):
                       continue
                 chosen_span_intervals.append((start_index, end_index)						)

                 if len(SCREAMING_SNAKE_CASE_						) == top_spans:
                       break
           return chosen_span_intervals


@add_end_docstrings(lowercase__)
class       A__						(    lowercase__			,  lowercase__):


     """simple docstring"""
     snake_case__						:       Optional[int]				    =VOCAB_FILES_NAMES
     snake_case__						:       Dict				    =READER_PRETRAINED_VOCAB_FILES_MAP
     snake_case__						:       Optional[Any]				    =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__						:       Tuple				    =READER_PRETRAINED_INIT_CONFIGURATION
     snake_case__						:       Dict				    =['''input_ids''', '''attention_mask''']
     snake_case__						:       Dict				    =DPRReaderTokenizer






"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer



					class 			lowercase					( lowercase__		):






								def __init__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	int = 101			)       ->	List[str]:





									"""simple docstring"""


									lowerCAmelCase												=  length






								def __len__(self  :	Any			)       ->	List[Any]:





									"""simple docstring"""


									return self.length






								def __getitem__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





									"""simple docstring"""


									return i





					class 			lowercase					:






								def __call__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





									"""simple docstring"""


									return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE_			), "labels": torch.tensor(SCREAMING_SNAKE_CASE_			)}





					class 			lowercase					( nn.Module		):






								def __init__(self  :	List[str]			)       ->	List[Any]:





									"""simple docstring"""


									super().__init__()
									# Add some (unused) params otherwise DDP will complain.
									lowerCAmelCase												=  nn.Linear(120      ,80			)






								def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None			)       ->	Union[str, Any]:





									"""simple docstring"""


									if labels is not None:
										return torch.tensor(0.0      ,device=input_ids.device			), input_ids
									else:
										return input_ids



class 			lowercase					( lowercase__		):






			@require_torch_neuroncore
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""--output_dir {output_dir}""".split()
				lowerCAmelCase												=  ['''torchrun'''] + distributed_args + args
				execute_subprocess_async(SCREAMING_SNAKE_CASE_      ,env=self.get_env()			)
				# successful return here == success - any errors would have caused an error in the sub-call





class 			lowercase					( lowercase__		):






			@require_torch_multi_gpu
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""--output_dir {output_dir}""".split()
				lowerCAmelCase												=  ['''torchrun'''] + distributed_args + args
				execute_subprocess_async(SCREAMING_SNAKE_CASE_      ,env=self.get_env()			)
				# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					UpperCAmelCase        =				HfArgumentParser((TrainingArguments,))
					UpperCAmelCase        =				parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
					    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [1_0_1, 4_0, 7]:
										UpperCAmelCase        =				DummyDataset(dataset_length)




										def __magic_name__	(				_lowerCamelCase:		EvalPrediction			)					->						Dict:

											'''simple docstring'''




											lowerCAmelCase												=  list(range(len(_lowerCamelCase			)			)			)
											lowerCAmelCase												=  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
											if not success and training_args.local_rank == 0:
												logger.warning(
												    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
												    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""			)
											return {"success": success}

										UpperCAmelCase        =				Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										UpperCAmelCase        =				trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										UpperCAmelCase        =				trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										UpperCAmelCase        =				2

										UpperCAmelCase        =				trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										UpperCAmelCase        =				trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)



										UpperCAmelCase        =				None


